Abstract

The integration of Internet of Things (IoT) and Artificial Intelligence (AI) technologies into Intensive Care Units (ICUs) was redefining critical care by introducing intelligent automation, real-time monitoring, and predictive capabilities. This chapter presents a comprehensive exploration of IoT-based smart ICU frameworks that incorporate AI-powered predictive maintenance and automated life support systems to enhance operational efficiency, patient safety, and clinical decision-making. It examines how interconnected biomedical devices, supported by edge computing and cloud-based analytics, enable continuous surveillance and autonomous therapeutic interventions. Emphasis was placed on AI-driven control models for mechanical ventilation, sedation, infusion management, and multimodal coordination, as well as predictive feedback loops that optimize resource utilization. The chapter also addresses key challenges in system integration, data security, validation, and ethical compliance, supported by a real-world case study demonstrating successful deployment of such technologies. Through this analysis, the work highlights scalable solutions and identifies future research directions to support the development of intelligent, adaptive, and patient-centric ICU environments. 

Introduction

The digitization of healthcare has reached a critical turning point with the integration of smart technologies into Intensive Care Units (ICUs), where precision, speed, and adaptability are paramount [1]. Traditional ICU setups rely heavily on human oversight, periodic measurements, and disparate systems that often operate in silos, creating inefficiencies and risks in the management of critically ill patients [2]. The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) presents a new model for ICU design—one that was fully interconnected, responsive, and capable of predictive decision-making [3]. IoT-enabled smart ICUs consist of networks of biomedical sensors, devices, and computational platforms that collect, analyze, and act on patient data in real time [4]. These systems allow for continuous physiological monitoring and operational feedback across medical equipment, laying the groundwork for datadriven and adaptive critical care environments [5]. A central feature of the smart ICU was the automation of life support systems such as mechanical ventilators, infusion pumps, and sedation management technologies [6]. These components, when enhanced by AI, can transition from manual or semi-automated control to intelligent systems that adjust therapeutic interventions autonomously based on patient-specific variables [7]. AI algorithms analyze trends and anomalies in patient data to recommend or directly implement clinical adjustments, reducing response time and the potential for human error [8]. This shift allows clinicians to move from task-based supervision to high-level decision-making roles while improving patient safety [9]. The integration of AI further enables pattern recognition in complex physiological signals, supporting early detection of critical deterioration and improving patient outcomes [10].